One of the best ways to improve your reach as a data scientist is to write functions. Functions allow you to automate common tasks. Writing a function has three big advantages over using copy-and-paste:
1. You drastistically reduce the chances of making incidental mistakes when
you copy and paste.
1. As requirements change, you only need to update code in one place, instead
of many.
1. You can give a function an evocative name that makes your code easier to
Writing good functions is a lifetime journey. Even after using R for many years we still learn new techniques and better ways of approaching old problems. The goal of this chapter is not to master every esoteric detail of functions but to get you started with some pragmatic advice that you can start using right away.
As well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code. Good coding style is like using correct punctuation. You can manage without it, but it sure makes things easier to read. As with styles of punctuation, there are many possible variations. Here we present the style we use in our code, but the most important thing is to be consistent.
You should consider writing a funtion whenever you've copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). For example, take a look at this code. What does it do?
You might be able to puzzle out that this rescales each column to have a range from 0 to 1. But did you spot the mistake? I made an error when copying-and-pasting the code for `df$b`: I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because it prevents you from making this type of mistake.
This code only has one input: `df$a`. (You might wonder if that `TRUE` is also an input: you can explore why it's not in the exercise below). To make the single input more clear, it's a good idea to rewrite the code using a temporary variables with a general name. Here this function only takes one vector of input, so I'll call it `x`:
Pulling out intermediate calculations into named variables is good practice because it makes it more clear what the code is doing. Now that I've simplified the code, and checked that it still works, I can turn it into a function:
Note the process that I followed here: I only made the function after I'd figured out how to make it work with a simple input. It's much easier to start with working code and turn it into a function as opposed to creating a function and then trying to make it work.
Compared to our original code, this is easier to understand and we've eliminated one class of copy-and-paste errors. There's still quite a bit of duplication since we're doing the same thing to multiple columns. You'll learn how to eliminate that duplication in the next chapter, Iteration.
It's important to remember that functions are not just for the computer, but are also for humans. R doesn't care what your function is called, or what comments it contains, but these are important for human readers. This section discusses some things that you should bear in mind when writing functions that humans can understand.
The name of a function is surprisingly important. Ideally the name of your function will be short, but clearly evoke what the function does. However, it's hard to come up with concise names, and autocomplete makes it easy to type long names, so it's better to err on the side of clear descriptions, rather than short names. There are a few exceptions to this rule: the handful of very common, very short names. It's worth memorising these:
Generally, function names should be verbs, and arguments should be nouns. There are some exceptions: nouns are ok if the function computes a very well known noun (i.e. `mean()` is better than `compute_mean()`), or accessing some property of an object (i.e. `coef()` is better than `get_coefficients()`). A good sign that a noun might be a better choice is if you're using a very broad verb like get, or compute, or calculate, or determine. Use your best judgement and don't be afraid to rename a function if you later figure out a better name.
To make it easy to type function names, I strongly recommend using only lowercase, and separating multiple words with underscores (so called "snake\_case"). Camel case is a legitimate alternative, but be consistent: pick either snake\_case or camelCase for your code, don't mix them. R itself is not very consistent, but there's nothing you can do about that. Make sure you don't fall into the same trap by making your code as consistent as possible.
If you have a family of functions that do similar things, make sure they have consistent names and arguments. Use a common prefix to indicate that they are connected. That's better than a common suffix because autocomplete allows you to type the prefix and see all the members of the family.
Where possible, avoid using names of common existing functions and variables. It's impossible to do in general because so many good names are already taken by other packages, but avoiding the most common names from base R will avoid confusion:
```{r, eval = FALSE}
# Don't do this!
T <- FALSE
c <- 10
mean <- function(x) sum(x)
```
Use comments, `#`, to explain the "why" of your code. You generally should avoid comments that explain the "what" or the "how". If you can't understand what the code does from reading, you should think about how to rewrite it to be more clear. Do you need to add some intermediate variables with useful names? Do you need to break out a subcomponent of a large function so you can describe it with a name? However, your code can never capture the reasoning behind your decisions: why do you choose this approach instead of an alternative? It's a great idea to capture that sort of thinking in a comment so that when you come back to your analysis in the future, you can jog your memory about the why.
Another important use of comments is to break up your file into easily readable chunks. Use long lines of `-` and `=` to make it easy to spot the breaks. RStudio even provides a keyboard shortcut to add this: Cmd/Ctrl + Shift + R.
```{r, eval = FALSE}
# Load data ---------------------------
# Plot data ---------------------------
```
### Exercises
1. Read the source code for each of the following three functions, puzzle out
what they do, and then brainstorm good names.
```{r}
f1 <- function(string, prefix) {
substr(string, 1, nchar(prefix)) == prefix
}
f2 <- function(x) {
if (length(x) <= 1L) return(NULL)
x[-length(x)]
}
f3 <- function(x, y) {
rep(y, length.out = length(x))
}
```
1. Take a function that you've written recently and spend 5 minutes
brainstorming a better name for it and its arguments.
Here's a simple function that uses an if statement. The goal of this function is to return a logical vector describing whether or not each element of a vector is named.
Squiggly brackets are always optional (both here and in function definitons), but I recommend using them because it makes it easier to see the hierarchy in your code. An opening curly brace should never go on its own line and should always be followed by a new line. A closing curly brace should always go on its own line, unless it's followed by `else`. Always indent the code inside curly braces.
If `condition` isn't a single `TRUE` or `FALSE` you'll get a warning or error.
You can use `||` (or) and `&&` (and) to combine multiple logical expressions. These operators a "short-circuiting": as soon as `||` sees the first `TRUE` it returns `TRUE` without computing anything else. As soon as `&&` sees the first `FALSE` it returns `FALSE`.
Like a function, an `if` statement "returns" the last expression it evaluated. This means you can assign the result of an `if` statement to a variable:
(Note there's a built in function that does this for you: `cut()`. The above call is the same as `cut(y, c(-Inf, 20, Inf), c("Too low", "Too high"))`. It returns a factor, and generalises better for large numbers of splits)
Note that arguments in R are lazily evaluated: they're not computed until they're needed. That means if they're never used, they're never called. This is an important property of R the programming language, but is unlikely to be important to you for a while. You can read more about lazy evaluation at <http://adv-r.had.co.nz/Functions.html#lazy-evaluation>
The arguments to a function typically fall into two broad sets: one set supplies the data to compute on, and the other supplies arguments that controls the details of the computation. For example:
Generally, data arguments should come first. Detail arguments should go on the end, and usually should have default values. You specify a default value in the same way you call a function with a named argument:
```{r}
# Compute standard error of a mean using normal approximation
The default value should almost always be the most common value. There are a few exceptions to do with safety. For example, it makes sense for `na.rm` to default to `FALSE` because missing values are important. Even though `na.rm = TRUE` is what you usually put in your code, it's a bad idea to silently ignoring missing values by default.
When you call a function, typically you can omit the names for the data arguments (because they are used so commonly). If you override the default value of a detail argument, you should use the full name:
You can refer to an argument by its unique prefix (e.g. `mean(x, n = TRUE)`), but this is generally best avoided given the possibilities for confusion.
Notice that when you call a function, you should place a space around `=` in function calls, and always put a space after a comma, not before (just like in regular English). Using whitespace makes it easier to skim the function for the important components.
There's one special argument you need to know about: `...`, pronounced dot-dot-dot. This captures any other arguments not otherwise matched. It's useful because you can then send those `...` on to another argument. This is a useful catch-all if your function primarily wraps another function.
Here `...` lets me forward on any arguments that I don't want to deal with to `paste()`. It's a very convenient technique. But it does came at a price: any misspelled arguments will not raise an error. This makes it easy for typos to go unnoticed:
The body of the function does the actual work. The value returned by the function is the last statement it evaluates. Unlike other languages all statements in R return a value.
You can explicitly return early from a function with `return()`. I think it's best to save the use of `return()` to signal that you can return early with a simpler solution. For example, you might write an if statement like this:
But if the first block is very long, by the time you get to the else, you've forgotten what's going on. One way to rewrite it is to use an early return for the simple case:
Invisible values are mostly used when your function is called primarily for its side-effects (e.g. printing, plotting, or saving a file). It's nice to be able pipe such functions together, so returning the main input value is useful. This allows you to do things like:
In many programming languages, this would be an error, because `y` is not defined inside the function. In R, this is valid code because R uses rules called lexical scoping to determine the value associated with a name. Since `y` is not defined inside the function, R will look where the function was defined:
This behaviour seems like a recipe for bugs, and indeed you should avoid creating functions like this deliberately, but by and large it doesn't cause too many problems (especially if you regularly restart R to get to a clean slate). The advantage of this behaviour is that from a language standpoint it allows R to be very consistent. Every name is looked up using the same set of rules. For `f()` that includes the behaviour of two things that you might not expect: `{` and `+`.
This is a common phenomenon in R. R gives you a lot of control. You can do many things that are not possible in other programming languages. You can things that 99% of the time extremely ill-advised (like overriding how addition works!), but this power and flexibility is what makes tools like ggplot2 and dplyr possible. Learning how to make good use of this flexibility is beyond the scope of this book, but you can read about in "Advanced R".
One challenge with writing functions is that many of the functions you've used in this book use non-standard evaluation to minimise typing. This makes these functions great for interactive use, but it does make it more challenging to program with them, because you need to use more advanced techniques. For example, imagine you'd written the following duplicated code across a handful of data analysis projects:
Writing reusable functions for ggplot2 poses a similar problem because `aes(group_var, mean_var)` would look for variables called `group_var` and `mean_var`. It's really only been in the last couple of months that I fully understood this problem, so there aren't currently any great (or general) solutions. However, now that I've understood the problem I think there will be some systematic solutions in the near future.
